Attempts to find central influencers, opinion leaders, hubs, optimal seeds, or other important people who can hasten or slow diffusion or social contagion has long been a major research question in network science. We demonstrate that opinion leadership occurs only under conventional but implausible scope conditions. We demonstrate that a highly central node is a more effective seed for diffusion than a random node if nodes can only learn via the network. However, actors are also subject to external influences such as mass media and advertising. We find that diffusion is noticeably faster when it begins with a high centrality node, but that this advantage only occurs in the region of parameter space where external influence is constrained to zero and collapses catastrophically even at minimal levels of external influence. Importantly, nearly all prior agent-based research on choosing a seed or seeds implicitly occurs in the network influence only region of parameter space. We demonstrate this effect using preferential attachment, small world, and several empirical networks. These networks vary in how large the baseline opinion leadership effect is, but in all of them it collapses with the introduction of external influence. This implies that, in marketing and public health, advertising broadly may be underrated as a strategy for promoting network-based diffusion.

Institutional leaders have long championed interdisciplinary research; however, researchers have paid relatively little attention to the people responding to such calls and their subsequent career outcomes. With the benefit of two large datasets spanning from 1986 through 2016, we show that interdisciplinary dissertations have become consistently more common in recent years as institutional leaders have highlighted the value of boundary-spanning research for solving important and emergent problems. With the benefit of survey data from a near-complete population of all dissertators in the US starting in 2001 through 2016, we observe a consistent upward trend in interdisciplinary dissertations. Unfortunately, we show that these interdisciplinary dissertators have experienced a comparably persistent penalty when considering salaries for their first year after earning the PhD. We also show that among interdisciplinary dissertators, individuals in lower-paying fields tend to earn more when choosing distantly related topic-combinations whereas researchers in higher-paying fields tend to be most rewarded for staying within relatively narrow disciplinary silos.

In author name disambiguation, author forenames are used to decide which name instances are disambiguated together and how much they are likely to refer to the same author. Despite such a crucial role of forenames, their effect on the performance of heuristic (string matching) and algorithmic disambiguation is not well understood. This study assesses the contributions of forenames in author name disambiguation using multiple labeled data sets under varying ratios and lengths of full forenames, reflecting real-world scenarios in which an author is represented by forename variants (synonym) and some authors share the same forenames (homonym). The results show that increasing the ratios of full forenames substantially improves both heuristic and machine-learning-based disambiguation. Performance gains by algorithmic disambiguation are pronounced when many forenames are initialized or homonyms are prevalent. As the ratios of full forenames increase, however, they become marginal compared to those by string matching. Using a small portion of forename strings does not reduce much the performances of both heuristic and algorithmic disambiguation methods compared to using full-length strings. These findings provide practical suggestions, such as restoring initialized forenames into a full-string format via record linkage for improved disambiguation performances. © 2019 ASIS&T

This technical note describes the results of a pilot approach to link administrative and survey data to better describe the richness and complexity of the research enterprise. In particular, we demonstrate how multiple funding channels can be studied by bringing together two disparate datasets: UMETRICS, which is based on university payroll and financial records, and the Survey of Earned Doctorates (SED), which is one of the most important US survey datasets about the doctoral workforce. We show how it is possible to link data on research funding and the doctorally qualified workforce to describe how many individuals are supported in different disciplines and by different agencies. We outline the potential for more work as the UMETRICS data expands to incorporate more linkages and more access is provided.

Clustering results in author name disambiguation are often evaluated by measures such as Cluster-F, K-metric, Pairwise-F, Splitting and Lumping Error, and B-cubed. Although these measures have different evaluation approaches, this paper shows that they can be calculated in a single framework by a set of common steps that compare truth and predicted clusters through two hash tables recording information about name instances with their predicted cluster indices and frequencies of those indices per truth cluster. This integrative calculation reduces greatly calculation runtime, which is scalable to a clustering task involving millions of name instances within a few seconds. During the integration process, B-cubed and K-metric are shown to produce the same precision and recall scores. In addition, name instance pairs for Pairwise-F are counted using a heuristic, which enables the proposed method to surpass a state-of-the-art algorithm in speedy calculation. Details of the integrative calculation are described with examples and pseudo-code to assist scholars to implement each measure easily and validate the correctness of implementation. The integrative calculation will help scholars compare similarities and differences of multiple measures before they select ones that characterize best the clustering performances of their disambiguation methods.

This paper uses transaction-based data to provide new insights into the link between the geographic proximity of businesses and associated economic activity. It develops two new measures of, and a set of stylized facts about, the distances between observed transactions between customers and vendors for a research intensive sector. Spending on research inputs is more likely with businesses physically closer to universities than those further away. Firms supplying a university project in one year are more likely to subsequently open an establishment near that university. Vendors who have supplied a project, are subsequently more likely to be a vendor on the same or related project.

We identify two polar life cycles of scholarly creativity among Nobel laureate economists with Tinbergen falling broadly in the middle. Experimental innovators work inductively, accumulating knowledge from experience. Conceptual innovators work deductively, applying abstract principles. Innovators whose work is more conceptual do their most important work earlier in their careers than those whose work is more experimental. Our estimates imply that the probability that the most conceptual laureate publishes his single best work peaks at age 25 compared to the mid-50 s for the most experimental laureate. Thus, while experience benefits experimental innovators, newness to a field benefits conceptual innovators.

Social networks represent two different facets of social life: (1) stable paths for diffusion, or the spread of something through a connected population, and (2) random draws from an underlying social space, which indicate the relative positions of the people in the network to one another. The dual nature of networks creates a challenge: if the observed network ties are a single random draw, is it realistic to expect that diffusion only follows the observed network ties? This study takes a first step toward integrating these two perspectives by introducing a social space diffusion model. In the model, network ties indicate positions in social space, and diffusion occurs proportionally to distance in social space. Practically, the simulation occurs in two parts. First, positions are estimated using a statistical model (in this example, a latent space model). Then, second, the predicted probabilities of a tie from that model-representing the distances in social space-or a series of networks drawn from those probabilities-representing routine churn in the network-are used as weights in a weighted averaging framework. Using longitudinal data from high school friendship networks, the author explores the properties of the model. The author shows that the model produces smoothed diffusion results, which predict attitudes in future waves 10 percent better than a diffusion model using the observed network and up to 5 percent better than diffusion models using alternative, non-model-based smoothing approaches.

In this issue, Kindel et al. describe a new approach to managing survey data in service of the Fragile Families Challenge, which they call “treating metadata as data.” Although the approach they present is a good first step, a more ambitious proposal could improve survey data analysis even more substantially. The author recommends that data collection efforts distribute an open-source set of tools for working with a particular data set the author calls data-specific functions. The goal of these functions is to codify best practices for working with the data in a set of functions for commonly used statistical software. These functions would be jointly developed by the users and distributers of the data. Building such functions would both shorten the learning curve for new users and improve the quality of the data, by making tacit knowledge about problems with the data explicit and easy to act on. © SAGE Publications Inc.. All rights reserved.